Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research.

Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth’s long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interactive analysis method, and we studied and analyzed the relationship between the air quality and several influencing factors, including meteorology, population, and economics. Six visualization methods were integrated in this study, each specifically designed and improved for visualization analysis purposes. To reveal the spatiotemporal distribution and potential impact of the air quality, we designed a comprehensive coupled visual interactive analysis approach visually express both high-dimensional and spatiotemporal attributes, reveal the overall situation and explain the relationship between attributes. We clarified the current spatiotemporal distribution, development trends, and influencing factors of the air quality in China through interactive visual analysis of a 25-dimensional dataset involving 31 Chinese provinces. We also verified the correctness and effectiveness of relevant policies and demonstrated the advantages of our method.

As the world has become increasingly digitalized in recent years, high-dimensional data with geographical location coordinate attributes, mainly referring to latitude and longitude, have been accumulated and spread to many disciplines. It is challenging to analyze such data. The map-in-parallel-coordinates plot (MPCP) is an incorporate visual analysis method that can express, filter, and highlight high-dimensional geographical data to facilitate data exploration and comprehension. In this paper, the MPCP underwent a series of field trial studies to verify its applicability, adaptability, and high efficacy in the real-world. The results of the evaluation were positive, which provides reasonable proof and new insights into the benefits of using MPCP to visually analyze high-dimensional geographical datasets.